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aaacc9bd68
* Update TensorRT-LLM --------- Co-authored-by: Starrick Liu <73152103+StarrickLiu@users.noreply.github.com>
88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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import transformers
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from ...mapping import Mapping
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from ..convert_utils import infer_dtype
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from ..modeling_utils import PretrainedConfig, QuantConfig
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class CohereConfig(PretrainedConfig):
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def __init__(self,
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*,
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output_multiplier_scale: float = 0.0625,
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rotary_base: float = 10000.0,
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attn_bias: bool = False,
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**kwargs):
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self.output_multiplier_scale = output_multiplier_scale
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self.rotary_base = rotary_base
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self.attn_bias = attn_bias
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super().__init__(**kwargs)
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def to_dict(self):
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output = super().to_dict()
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# Serialize the fields added in CohereConfig
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output['output_multiplier_scale'] = self.output_multiplier_scale
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output['rotary_base'] = self.rotary_base
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output['attn_bias'] = self.attn_bias
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return output
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@classmethod
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def from_hugging_face(
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cls,
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hf_config_or_dir: Union[str, 'transformers.PretrainedConfig'],
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dtype: str = 'auto',
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mapping: Optional[Mapping] = None,
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quant_config: Optional[QuantConfig] = None,
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**kwargs):
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if isinstance(hf_config_or_dir, transformers.PretrainedConfig):
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hf_config = hf_config_or_dir
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else:
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hf_config = transformers.AutoConfig.from_pretrained(
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hf_config_or_dir, trust_remote_code=True)
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head_size = hf_config.hidden_size // hf_config.num_attention_heads
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dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
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if hf_config.tie_word_embeddings:
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kwargs['use_parallel_embedding'] = True
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kwargs['embedding_sharding_dim'] = 0
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return CohereConfig(
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architecture=hf_config.architectures[0],
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dtype=dtype,
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num_hidden_layers=hf_config.num_hidden_layers,
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num_attention_heads=hf_config.num_attention_heads,
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hidden_size=hf_config.hidden_size,
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intermediate_size=hf_config.intermediate_size,
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num_key_value_heads=hf_config.num_key_value_heads,
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head_size=head_size,
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vocab_size=hf_config.vocab_size,
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position_embedding_type='rope_gptj', # different rope type
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max_position_embeddings=hf_config.max_position_embeddings,
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hidden_act=hf_config.hidden_act,
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norm_epsilon=hf_config.layer_norm_eps,
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output_multiplier_scale=hf_config.logit_scale,
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rotary_base=hf_config.rope_theta,
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attn_bias=hf_config.attention_bias,
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qk_layernorm=hf_config.use_qk_norm,
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mapping=mapping,
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quantization=quant_config,
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**kwargs)
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